R Notebook

df <- read.csv("merged-new-version2.csv", header =TRUE, sep=",")
#df <- df[!complete.cases(df), ]  
df

df$ln_novelty <- log(df$novelty+1)
df$ln_total <- log(df$total+1) 
df$ln_exploration <- log(df$exploration+1) 
df$group = factor(df$group)
df

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_exploration ~ factor(group), data=df)
summary(mod)

Call:
lm(formula = ln_exploration ~ factor(group), data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.2275 -0.1862 -0.1563  0.1866  0.5328 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.22749    0.01941  11.719   <2e-16 ***
factor(group)0 -0.06718    0.02710  -2.479   0.0134 *  
factor(group)1 -0.04128    0.02678  -1.542   0.1236    
factor(group)2 -0.03052    0.02662  -1.146   0.2521    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2362 on 632 degrees of freedom
Multiple R-squared:  0.009905,  Adjusted R-squared:  0.005206 
F-statistic: 2.108 on 3 and 632 DF,  p-value: 0.09805

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_total ~ factor(group), data=df)
summary(mod)

Call:
lm(formula = ln_total ~ factor(group), data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.7373 -0.2143  0.3493  0.8471  1.7667 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)      5.1441     0.1181  43.541  < 2e-16 ***
factor(group)0  -1.0417     0.1649  -6.316 5.05e-10 ***
factor(group)1  -0.4069     0.1630  -2.497 0.012787 *  
factor(group)2  -0.5990     0.1620  -3.697 0.000237 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.437 on 632 degrees of freedom
Multiple R-squared:  0.06155,   Adjusted R-squared:  0.0571 
F-statistic: 13.82 on 3 and 632 DF,  p-value: 9.76e-09

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group), data=df)
summary(mod)

Call:
lm(formula = ln_novelty ~ factor(group), data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.52892 -0.14068  0.06865  0.15783  0.28954 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.52892    0.01773  29.837  < 2e-16 ***
factor(group)0 -0.13269    0.02475  -5.362 1.16e-07 ***
factor(group)1 -0.12367    0.02445  -5.058 5.56e-07 ***
factor(group)2 -0.05178    0.02431  -2.130   0.0336 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2157 on 632 degrees of freedom
Multiple R-squared:  0.05844,   Adjusted R-squared:  0.05397 
F-statistic: 13.08 on 3 and 632 DF,  p-value: 2.706e-08

df$group <- relevel(df$group, ref = "3")
mod2 <- lm(ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod2)

Call:
lm(formula = ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + 
    Q8_Q8_1 + Q10 + count, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.6625 -0.1580 -0.1158  0.1618  0.5694 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.233602   0.038414   6.081 2.10e-09 ***
factor(group)0 -0.055198   0.026422  -2.089   0.0371 *  
factor(group)1 -0.036783   0.026092  -1.410   0.1591    
factor(group)2 -0.022188   0.025726  -0.862   0.3888    
Q7_Q7_1        -0.003198   0.007597  -0.421   0.6740    
Q7_Q7_2         0.005396   0.007728   0.698   0.4853    
Q8_Q8_1        -0.013705   0.007994  -1.714   0.0870 .  
Q10            -0.003711   0.011739  -0.316   0.7520    
count           0.025482   0.003090   8.248 9.92e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2257 on 611 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.1106,    Adjusted R-squared:  0.09895 
F-statistic: 9.497 on 8 and 611 DF,  p-value: 1.997e-12

df$group <- relevel(df$group, ref = "3")
mod3 <- lm(ln_exploration ~  Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod3)

Call:
lm(formula = ln_exploration ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + 
    count, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.6609 -0.1563 -0.1278  0.1708  0.5594 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.201974   0.033922   5.954 4.40e-09 ***
Q7_Q7_1     -0.003721   0.007550  -0.493    0.622    
Q7_Q7_2      0.006447   0.007670   0.841    0.401    
Q8_Q8_1     -0.012414   0.007974  -1.557    0.120    
Q10         -0.006051   0.011524  -0.525    0.600    
count        0.025721   0.003089   8.326 5.47e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.226 on 614 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.1038,    Adjusted R-squared:  0.09647 
F-statistic: 14.22 on 5 and 614 DF,  p-value: 3.509e-13

anova(mod2, mod3)

Analysis of Variance Table

Model 1: ln_exploration ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + 
    Q10 + count
Model 2: ln_exploration ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
  Res.Df    RSS Df Sum of Sq      F Pr(>F)
1    611 31.133                           
2    614 31.372 -3  -0.23919 1.5647 0.1968

df$group <- relevel(df$group, ref = "3")
mod <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod)

Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + 
    Q8_Q8_1 + Q10 + count, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.73108 -0.10789  0.05269  0.14730  0.30517 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.412100   0.035171  11.717  < 2e-16 ***
factor(group)0 -0.113961   0.024192  -4.711 3.06e-06 ***
factor(group)1 -0.116408   0.023889  -4.873 1.40e-06 ***
factor(group)2 -0.051286   0.023555  -2.177  0.02984 *  
Q7_Q7_1        -0.020611   0.006956  -2.963  0.00316 ** 
Q7_Q7_2         0.028904   0.007075   4.085 4.99e-05 ***
Q8_Q8_1         0.008860   0.007319   1.210  0.22656    
Q10             0.007122   0.010748   0.663  0.50783    
count           0.013293   0.002829   4.699 3.23e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2067 on 611 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.1234,    Adjusted R-squared:  0.112 
F-statistic: 10.75 on 8 and 611 DF,  p-value: 3.249e-14

df$group <- relevel(df$group, ref = "3")
mod1 <- lm(ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod1)

Call:
lm(formula = ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + 
    Q8_Q8_1 + Q10 + count, data = df)

Residuals:
     Min       1Q   Median       3Q      Max 
-0.73108 -0.10789  0.05269  0.14730  0.30517 

Coefficients:
                Estimate Std. Error t value Pr(>|t|)    
(Intercept)     0.412100   0.035171  11.717  < 2e-16 ***
factor(group)0 -0.113961   0.024192  -4.711 3.06e-06 ***
factor(group)1 -0.116408   0.023889  -4.873 1.40e-06 ***
factor(group)2 -0.051286   0.023555  -2.177  0.02984 *  
Q7_Q7_1        -0.020611   0.006956  -2.963  0.00316 ** 
Q7_Q7_2         0.028904   0.007075   4.085 4.99e-05 ***
Q8_Q8_1         0.008860   0.007319   1.210  0.22656    
Q10             0.007122   0.010748   0.663  0.50783    
count           0.013293   0.002829   4.699 3.23e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2067 on 611 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.1234,    Adjusted R-squared:  0.112 
F-statistic: 10.75 on 8 and 611 DF,  p-value: 3.249e-14

df$group <- relevel(df$group, ref = "3")
mod4 <- lm(ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod4)

Call:
lm(formula = ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + 
    count, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-0.7883 -0.0854  0.0699  0.1531  0.3014 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept)  0.343113   0.031746  10.808  < 2e-16 ***
Q7_Q7_1     -0.023135   0.007066  -3.274  0.00112 ** 
Q7_Q7_2      0.032111   0.007178   4.474 9.17e-06 ***
Q8_Q8_1      0.011171   0.007462   1.497  0.13490    
Q10         -0.001228   0.010785  -0.114  0.90939    
count        0.013646   0.002891   4.720 2.93e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2115 on 614 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.07716,   Adjusted R-squared:  0.06964 
F-statistic: 10.27 on 5 and 614 DF,  p-value: 1.82e-09

anova(mod1, mod4)

Analysis of Variance Table

Model 1: ln_novelty ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + 
    count
Model 2: ln_novelty ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
  Res.Df    RSS Df Sum of Sq      F    Pr(>F)    
1    611 26.099                                  
2    614 27.477 -3   -1.3777 10.751 6.815e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

library(lmerTest)
fit.lmer <- lmer(ln_novelty ~ factor(group) + ( 1 | phase), data = df, REML= FALSE)
fit.lmer

Linear mixed model fit by maximum likelihood  ['lmerModLmerTest']
Formula: ln_novelty ~ factor(group) + (1 | phase)
   Data: df
      AIC       BIC    logLik  deviance  df.resid 
-138.4479 -111.7167   75.2239 -150.4479       630 
Random effects:
 Groups   Name        Std.Dev.
 phase    (Intercept) 0.005242
 Residual             0.214918
Number of obs: 636, groups:  phase, 4
Fixed Effects:
   (Intercept)  factor(group)0  factor(group)1  factor(group)2  
       0.52892        -0.13269        -0.12367        -0.05178  

tapply(df$ln_novelty, df$group, summary)

$`3`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.4842  0.5588  0.5289  0.6162  0.6894 

$`0`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.5206  0.3962  0.6073  0.6858 

$`1`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.1777  0.5062  0.4053  0.6182  0.6931 

$`2`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.3871  0.5465  0.4771  0.6084  0.6904 

tapply(df$ln_total, df$group, summary)

$`3`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  4.331   4.761   5.079   5.144   5.515   5.891 

$`0`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   3.991   4.830   4.102   5.337   5.869 

$`1`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   4.553   5.089   4.737   5.580   5.882 

$`2`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  0.000   4.615   4.925   4.545   5.450   5.884 

tapply(df$ln_exploration, df$group, summary)

$`3`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0938  0.2275  0.4391  0.6931 

$`0`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.0000  0.1603  0.3010  0.6931 

$`1`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.02175 0.18621 0.38244 0.69315 

$`2`
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
0.00000 0.00000 0.09391 0.19697 0.35899 0.69315 

library(vtree)

Registered S3 methods overwritten by 'htmltools':
  method               from         
  print.html           tools:rstudio
  print.shiny.tag      tools:rstudio
  print.shiny.tag.list tools:rstudio
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio
vtree version 5.6.5 -- For more information, type: vignette("vtree")

vtree(df, "group")

vtree(df, c("phase", "group"), 
   fillcolor = c( phase = "#e7d4e8", group = "#99d8c9"),
   horiz = FALSE)

df$group <- relevel(df$group, ref = "3")
mod5 <- lm(ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod5)

Call:
lm(formula = ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + 
    Q10 + count, data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.6309 -0.2310  0.3346  0.7764  1.9667 

Coefficients:
               Estimate Std. Error t value Pr(>|t|)    
(Intercept)     4.82832    0.22926  21.060  < 2e-16 ***
factor(group)0 -0.98353    0.15769  -6.237 8.33e-10 ***
factor(group)1 -0.42360    0.15572  -2.720 0.006709 ** 
factor(group)2 -0.59841    0.15354  -3.897 0.000108 ***
Q7_Q7_1        -0.19585    0.04534  -4.319 1.83e-05 ***
Q7_Q7_2         0.19627    0.04612   4.256 2.41e-05 ***
Q8_Q8_1        -0.10504    0.04771  -2.202 0.028060 *  
Q10             0.17920    0.07006   2.558 0.010776 *  
count           0.12749    0.01844   6.914 1.19e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.347 on 611 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.1768,    Adjusted R-squared:  0.166 
F-statistic:  16.4 on 8 and 611 DF,  p-value: < 2.2e-16

df$group <- relevel(df$group, ref = "3")
mod6 <- lm(ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, data=df)
summary(mod6)

Call:
lm(formula = ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count, 
    data = df)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.5737 -0.1258  0.3665  0.7666  1.7353 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  4.19765    0.20821  20.160  < 2e-16 ***
Q7_Q7_1     -0.18970    0.04634  -4.093 4.82e-05 ***
Q7_Q7_2      0.19885    0.04708   4.224 2.77e-05 ***
Q8_Q8_1     -0.07884    0.04894  -1.611   0.1077    
Q10          0.17509    0.07073   2.475   0.0136 *  
count        0.13321    0.01896   7.025 5.71e-12 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.387 on 614 degrees of freedom
  (16 observations deleted due to missingness)
Multiple R-squared:  0.1226,    Adjusted R-squared:  0.1154 
F-statistic: 17.16 on 5 and 614 DF,  p-value: 6.62e-16

anova(mod5, mod6)

Analysis of Variance Table

Model 1: ln_total ~ factor(group) + Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + 
    count
Model 2: ln_total ~ Q7_Q7_1 + Q7_Q7_2 + Q8_Q8_1 + Q10 + count
  Res.Df  RSS Df Sum of Sq      F    Pr(>F)    
1    611 1109                                  
2    614 1182 -3   -73.013 13.409 1.744e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

with(df, interaction.plot(group, phase, ln_total, ylim=c(0, max(ln_total)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot

with(df, interaction.plot(group, phase, ln_exploration, ylim=c(0, max(ln_exploration)))) # interaction plot

with(df, interaction.plot(group, phase, ln_novelty, ylim=c(0, max(ln_novelty)))) # interaction plot